Dr. Reza Khaninezhad

Dr. Reza Khaninezhad

Data Science Manager Apache Corporation

As the Data Science Manager at Apache Corporation, Reza spearhead the development and deployment of scalable AI systems that enhance operational efficiency and deliver actionable insights. His work involves leveraging predictive modeling, time-series analytics, and distributed computing to address complex business challenges and integrate advanced technologies into workflows, optimizing decision-making processes.

With over five years of experience in venture capital, Reza collaborates with technology startups to scale innovative solutions. This dual expertise in data science and strategic investments positions him at the intersection of AI and business transformation, where he aims to foster innovation.

He is passionate about building systems that not only process data but also generate tangible value, bridging the gap between complex algorithms and real-world applications. 

Key Highlights:

  • Led initiatives at Apache Corporation to design and implement AI systems that optimize operations and drive insights.
  • Developed and deployed large-scale AI solutions generating revenue and reducing operational costs.
  • Collaborated with startups in the technology sector, providing strategic investment insights and scaling innovative solutions.
  • Active speaker at industry conferences, sharing expertise on AI applications in energy and technology sectors.

Main Conference Day One - November 5

2:10 PM AI in Practice at Apache: Aligning Use Cases with Business Strategy

Apache has developed a clear AI roadmap focused on delivering measurable business value. By prioritizing high-impact projects and applying a structured framework that defines success criteria from the outset, Apache ensures its AI initiatives are scalable and adaptable to rapid technological advancements.

In this session, Reza will present three high-ROI use cases that illustrate this approach:
  • Remote Operations Agentic Summarization:
Sythensising key events, alarms and notifications from ROC’s
Results: Time savings and an incident response time of 10s%
Lessons Learnt: Clarity and context in summaries vital for decision effectiveness
  • Power & Energy Demand Modeling and Optimization:
Informing asset & equipment decisions based on power demand
Results: Energy cost reduction
Lessons Learnt: The importance of the granularity of data and how predictive accuracy boosts efficiency
  • Water Takeaway Modeling & Decision Support:
Implementing water cut and WOR ‘creaming curves’ to determine how to best route and prioritize production in water-constrained environments
Results: Penalty reductions and operational efficiency
Lessons Learnt: Why data accuracy is critical and how real-time updates enhance forecasting reliability 

Main Conference Day Two - November 6

11:40 AM Panel Discussion: Technology Adoption vs Technology Innovation: When is it Right to Deaccelerate Your Digital Transformation Journey?

  • Balancing the need to maximize existing technologies and ensure gradual workplace adoption while also maintaining the pace of innovation 
  • Understanding the importance of evidential outcomes to build frontline workers' confidence in new tech 
  • Consistently capturing data to ensure long-term ROI from current technology stacks 
  • Strategically managing the risks of adopting a slower innovation pace, considering market pressures 

Check out the incredible speaker line-up to see who will be joining Dr. Reza.

Download The Latest Agenda